code: def _conv2d(
      channel,
      kernel=1,
      padding=0,
      stride=1,
      num_group=1,
      use_act=True,
      use_bias=True,
      norm_layer=BatchNorm,
      norm_kwargs=None,
  ):
      cell = nn.HybridSequential(prefix="")
      cell.add(
          nn.Conv2D(
              channel,
              kernel_size=kernel,
              strides=stride,
              padding=padding,
              groups=num_group,
              use_bias=use_bias,
          )
      )
      cell.add(
          norm_layer(
              epsilon=1e-5,
              momentum=0.9,
              **({} if norm_kwargs is None else norm_kwargs),
          )
      )
      if use_act:
          cell.add(nn.Activation("relu"))
      return cell
  
imports: from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 1 for calling a global function with example usage: _conv2d(channel, kernel, padding, stride, num_group, use_act, use_bias, norm_layer, norm_kwargs) and returns: cell